{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of the GBM GridSearchCV results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file illustrates how to evaluate the [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) results for sklearn's [GradientBoostingClassifier](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) obtained after first running `sklearn_gbm_tuning.py` in this directory to test various hyperparameter combinations and store the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.151178Z",
     "start_time": "2020-06-19T23:53:13.149497Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.767020Z",
     "start_time": "2020-06-19T23:53:13.152783Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "import os\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import graphviz\n",
    "\n",
    "from statsmodels.api import OLS, add_constant\n",
    "from sklearn.tree import DecisionTreeRegressor, export_graphviz\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.770487Z",
     "start_time": "2020-06-19T23:53:13.768278Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style(\"white\")\n",
    "np.random.seed(42)\n",
    "pd.options.display.float_format = '{:,.4f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.831433Z",
     "start_time": "2020-06-19T23:53:13.771314Z"
    }
   },
   "outputs": [],
   "source": [
    "with pd.HDFStore('data/tuning_sklearn_gbm.h5') as store:\n",
    "    test_feature_data = store['holdout/features']\n",
    "    test_features = test_feature_data.columns\n",
    "    test_target = store['holdout/target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GBM GridsearchCV with sklearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need OneStepTimeSeriesSplit because stored GridSearchCV result expects it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.834022Z",
     "start_time": "2020-06-19T23:53:13.832311Z"
    }
   },
   "outputs": [],
   "source": [
    "class OneStepTimeSeriesSplit:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to first run `sklearn_gbm_tuning.py` to perform gridsearch and store result (not included due to file size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.890118Z",
     "start_time": "2020-06-19T23:53:13.834893Z"
    }
   },
   "outputs": [],
   "source": [
    "gridsearch_result = joblib.load('results/sklearn_gbm_gridsearch.joblib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GridSearchCV object has several additional attributes after completion that we can access after loading the pickled result to learn which hyperparameter combination performed best and its average cross-validation AUC score, which results in a modest improvement over the default values. This is shown in the following code:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Parameters & AUC Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.895742Z",
     "start_time": "2020-06-19T23:53:13.891528Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "learning_rate             0.0100\n",
       "max_depth                 9.0000\n",
       "max_features              1.0000\n",
       "min_impurity_decrease     0.0100\n",
       "min_samples_split        50.0000\n",
       "n_estimators            300.0000\n",
       "subsample                 1.0000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(gridsearch_result.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.904087Z",
     "start_time": "2020-06-19T23:53:13.896807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.5569'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{gridsearch_result.best_score_:.4f}'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test on hold-out set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.911652Z",
     "start_time": "2020-06-19T23:53:13.904914Z"
    }
   },
   "outputs": [],
   "source": [
    "best_model = gridsearch_result.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:13.920291Z",
     "start_time": "2020-06-19T23:53:13.912695Z"
    }
   },
   "outputs": [],
   "source": [
    "idx = pd.IndexSlice\n",
    "test_dates = sorted(test_feature_data.index.get_level_values('date').unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.066465Z",
     "start_time": "2020-06-19T23:53:13.921156Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "auc = {}\n",
    "for i, test_date in enumerate(test_dates):\n",
    "    test_data = test_feature_data.loc[idx[:, test_date], :]\n",
    "    preds = best_model.predict(test_data)\n",
    "    auc[i] = roc_auc_score(y_true=test_target.loc[test_data.index], y_score=preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.069511Z",
     "start_time": "2020-06-19T23:53:14.067418Z"
    }
   },
   "outputs": [],
   "source": [
    "auc = pd.Series(auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.080391Z",
     "start_time": "2020-06-19T23:53:14.070451Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   0.5381\n",
       "1   0.5163\n",
       "2   0.5257\n",
       "3   0.5061\n",
       "4   0.5235\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.312427Z",
     "start_time": "2020-06-19T23:53:14.081241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = auc.sort_index(ascending=False).plot.barh(xlim=(.45, .55),\n",
    "                                               title=f'Test AUC: {auc.mean():.2%}',\n",
    "                                               figsize=(8, 4))\n",
    "ax.axvline(auc.mean(), ls='--', lw=1, c='k')\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect global feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.548848Z",
     "start_time": "2020-06-19T23:53:14.313411Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.Series(best_model.feature_importances_,\n",
    "           index=test_features)\n",
    " .sort_values()\n",
    " .tail(25)\n",
    " .plot.barh(figsize=(8, 5)))\n",
    "sns.despine()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CV Train-Test Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.567048Z",
     "start_time": "2020-06-19T23:53:14.549959Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 40 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   mean_fit_time                576 non-null    float64\n",
      " 1   std_fit_time                 576 non-null    float64\n",
      " 2   mean_score_time              576 non-null    float64\n",
      " 3   std_score_time               576 non-null    float64\n",
      " 4   param_learning_rate          576 non-null    object \n",
      " 5   param_max_depth              576 non-null    object \n",
      " 6   param_max_features           576 non-null    object \n",
      " 7   param_min_impurity_decrease  576 non-null    object \n",
      " 8   param_min_samples_split      576 non-null    object \n",
      " 9   param_n_estimators           576 non-null    object \n",
      " 10  param_subsample              576 non-null    object \n",
      " 11  split0_test_score            576 non-null    float64\n",
      " 12  split1_test_score            576 non-null    float64\n",
      " 13  split2_test_score            576 non-null    float64\n",
      " 14  split3_test_score            576 non-null    float64\n",
      " 15  split4_test_score            576 non-null    float64\n",
      " 16  split5_test_score            576 non-null    float64\n",
      " 17  split6_test_score            576 non-null    float64\n",
      " 18  split7_test_score            576 non-null    float64\n",
      " 19  split8_test_score            576 non-null    float64\n",
      " 20  split9_test_score            576 non-null    float64\n",
      " 21  split10_test_score           576 non-null    float64\n",
      " 22  split11_test_score           576 non-null    float64\n",
      " 23  mean_test_score              576 non-null    float64\n",
      " 24  std_test_score               576 non-null    float64\n",
      " 25  rank_test_score              576 non-null    int32  \n",
      " 26  split0_train_score           576 non-null    float64\n",
      " 27  split1_train_score           576 non-null    float64\n",
      " 28  split2_train_score           576 non-null    float64\n",
      " 29  split3_train_score           576 non-null    float64\n",
      " 30  split4_train_score           576 non-null    float64\n",
      " 31  split5_train_score           576 non-null    float64\n",
      " 32  split6_train_score           576 non-null    float64\n",
      " 33  split7_train_score           576 non-null    float64\n",
      " 34  split8_train_score           576 non-null    float64\n",
      " 35  split9_train_score           576 non-null    float64\n",
      " 36  split10_train_score          576 non-null    float64\n",
      " 37  split11_train_score          576 non-null    float64\n",
      " 38  mean_train_score             576 non-null    float64\n",
      " 39  std_train_score              576 non-null    float64\n",
      "dtypes: float64(32), int32(1), object(7)\n",
      "memory usage: 177.9+ KB\n"
     ]
    }
   ],
   "source": [
    "results = pd.DataFrame(gridsearch_result.cv_results_).drop('params', axis=1)\n",
    "results.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.624781Z",
     "start_time": "2020-06-19T23:53:14.568130Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_learning_rate</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_min_impurity_decrease</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>...</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>split5_train_score</th>\n",
       "      <th>split6_train_score</th>\n",
       "      <th>split7_train_score</th>\n",
       "      <th>split8_train_score</th>\n",
       "      <th>split9_train_score</th>\n",
       "      <th>split10_train_score</th>\n",
       "      <th>split11_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0867</td>\n",
       "      <td>1.1143</td>\n",
       "      <td>0.0076</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6734</td>\n",
       "      <td>0.6724</td>\n",
       "      <td>0.6715</td>\n",
       "      <td>0.6711</td>\n",
       "      <td>0.6773</td>\n",
       "      <td>0.6722</td>\n",
       "      <td>0.6774</td>\n",
       "      <td>0.6814</td>\n",
       "      <td>0.6721</td>\n",
       "      <td>0.0049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.4386</td>\n",
       "      <td>1.3369</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6654</td>\n",
       "      <td>0.6744</td>\n",
       "      <td>0.6748</td>\n",
       "      <td>0.6706</td>\n",
       "      <td>0.6847</td>\n",
       "      <td>0.6762</td>\n",
       "      <td>0.6747</td>\n",
       "      <td>0.6786</td>\n",
       "      <td>0.6730</td>\n",
       "      <td>0.0059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.3189</td>\n",
       "      <td>3.4796</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6850</td>\n",
       "      <td>0.6867</td>\n",
       "      <td>0.6885</td>\n",
       "      <td>0.6864</td>\n",
       "      <td>0.6889</td>\n",
       "      <td>0.6895</td>\n",
       "      <td>0.6943</td>\n",
       "      <td>0.6958</td>\n",
       "      <td>0.6865</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93.7252</td>\n",
       "      <td>5.0023</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6852</td>\n",
       "      <td>0.6855</td>\n",
       "      <td>0.6921</td>\n",
       "      <td>0.6896</td>\n",
       "      <td>0.6874</td>\n",
       "      <td>0.6857</td>\n",
       "      <td>0.6937</td>\n",
       "      <td>0.6964</td>\n",
       "      <td>0.6870</td>\n",
       "      <td>0.0050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.9732</td>\n",
       "      <td>1.5380</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>3</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>0.6671</td>\n",
       "      <td>0.6807</td>\n",
       "      <td>0.6759</td>\n",
       "      <td>0.6752</td>\n",
       "      <td>0.6707</td>\n",
       "      <td>0.6767</td>\n",
       "      <td>0.6830</td>\n",
       "      <td>0.6789</td>\n",
       "      <td>0.6736</td>\n",
       "      <td>0.0061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        27.0867        1.1143           0.0076          0.0007   \n",
       "1        31.4386        1.3369           0.0074          0.0006   \n",
       "2        81.3189        3.4796           0.0118          0.0009   \n",
       "3        93.7252        5.0023           0.0115          0.0006   \n",
       "4        26.9732        1.5380           0.0073          0.0004   \n",
       "\n",
       "  param_learning_rate param_max_depth param_max_features  \\\n",
       "0              0.0100               3               sqrt   \n",
       "1              0.0100               3               sqrt   \n",
       "2              0.0100               3               sqrt   \n",
       "3              0.0100               3               sqrt   \n",
       "4              0.0100               3               sqrt   \n",
       "\n",
       "  param_min_impurity_decrease param_min_samples_split param_n_estimators  ...  \\\n",
       "0                           0                      10                100  ...   \n",
       "1                           0                      10                100  ...   \n",
       "2                           0                      10                300  ...   \n",
       "3                           0                      10                300  ...   \n",
       "4                           0                      50                100  ...   \n",
       "\n",
       "  split4_train_score  split5_train_score  split6_train_score  \\\n",
       "0             0.6734              0.6724              0.6715   \n",
       "1             0.6654              0.6744              0.6748   \n",
       "2             0.6850              0.6867              0.6885   \n",
       "3             0.6852              0.6855              0.6921   \n",
       "4             0.6671              0.6807              0.6759   \n",
       "\n",
       "   split7_train_score  split8_train_score  split9_train_score  \\\n",
       "0              0.6711              0.6773              0.6722   \n",
       "1              0.6706              0.6847              0.6762   \n",
       "2              0.6864              0.6889              0.6895   \n",
       "3              0.6896              0.6874              0.6857   \n",
       "4              0.6752              0.6707              0.6767   \n",
       "\n",
       "   split10_train_score  split11_train_score  mean_train_score  std_train_score  \n",
       "0               0.6774               0.6814            0.6721           0.0049  \n",
       "1               0.6747               0.6786            0.6730           0.0059  \n",
       "2               0.6943               0.6958            0.6865           0.0052  \n",
       "3               0.6937               0.6964            0.6870           0.0050  \n",
       "4               0.6830               0.6789            0.6736           0.0061  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get parameter values & mean test scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.639686Z",
     "start_time": "2020-06-19T23:53:14.625816Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 8 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   learning_rate          576 non-null    object \n",
      " 1   max_depth              576 non-null    object \n",
      " 2   max_features           576 non-null    object \n",
      " 3   min_impurity_decrease  576 non-null    object \n",
      " 4   min_samples_split      576 non-null    object \n",
      " 5   n_estimators           576 non-null    object \n",
      " 6   subsample              576 non-null    object \n",
      " 7   test_score             576 non-null    float64\n",
      "dtypes: float64(1), object(7)\n",
      "memory usage: 36.1+ KB\n"
     ]
    }
   ],
   "source": [
    "test_scores = results.filter(like='param').join(results[['mean_test_score']])\n",
    "test_scores = test_scores.rename(columns={c: '_'.join(c.split('_')[1:]) for c in test_scores.columns})\n",
    "test_scores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.647729Z",
     "start_time": "2020-06-19T23:53:14.640683Z"
    }
   },
   "outputs": [],
   "source": [
    "params = test_scores.columns[:-1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.663222Z",
     "start_time": "2020-06-19T23:53:14.648723Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_score</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>learning_rate</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>max_depth</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>max_features</td>\n",
       "      <td>sqrt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>min_impurity_decrease</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5151</td>\n",
       "      <td>min_samples_split</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_score              parameter  value\n",
       "0      0.5151          learning_rate 0.0100\n",
       "1      0.5151              max_depth      3\n",
       "2      0.5151           max_features   sqrt\n",
       "3      0.5151  min_impurity_decrease      0\n",
       "4      0.5151      min_samples_split     10"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores = test_scores.set_index('test_score').stack().reset_index()\n",
    "test_scores.columns= ['test_score', 'parameter', 'value']\n",
    "test_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.670076Z",
     "start_time": "2020-06-19T23:53:14.665640Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4032 entries, 0 to 4031\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   test_score  4032 non-null   float64\n",
      " 1   parameter   4032 non-null   object \n",
      " 2   value       4032 non-null   object \n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "test_scores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.678135Z",
     "start_time": "2020-06-19T23:53:14.671044Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_test_scores(df):\n",
    "    \"\"\"Select parameter values and test scores\"\"\"\n",
    "    data = df.filter(like='param').join(results[['mean_test_score']])\n",
    "    return data.rename(columns={c: '_'.join(c.split('_')[1:]) for c in data.columns})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Test Scores vs Parameter Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GridSearchCV result stores the average cross-validation scores so that we can analyze how different hyperparameter settings affect the outcome.\n",
    "\n",
    "The six seaborn swarm plots below show the distribution of AUC test scores for all parameter values. In this case, the highest AUC  test scores required a low learning_rate and a large value for max_features. Some parameter settings, such as a low learning_rate, produce a wide range of outcomes that depend on the complementary settings of other parameters. Other parameters are compatible with high scores for all settings use in the experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:14.690854Z",
     "start_time": "2020-06-19T23:53:14.679040Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   learning_rate      576 non-null    object \n",
      " 1   max_depth          576 non-null    object \n",
      " 2   max_features       576 non-null    object \n",
      " 3   min_samples_split  576 non-null    object \n",
      " 4   n_estimators       576 non-null    object \n",
      " 5   subsample          576 non-null    object \n",
      " 6   test_score         576 non-null    float64\n",
      "dtypes: float64(1), object(6)\n",
      "memory usage: 31.6+ KB\n"
     ]
    }
   ],
   "source": [
    "plot_data = get_test_scores(results).drop('min_impurity_decrease', axis=1)\n",
    "plot_params = plot_data.columns[:-1].tolist()\n",
    "plot_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:16.915123Z",
     "start_time": "2020-06-19T23:53:14.691735Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(12, 6))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, param in enumerate(plot_params):\n",
    "    sns.swarmplot(x=param, y='test_score', data=plot_data, ax=axes[i])\n",
    "    \n",
    "fig.suptitle('Mean Test Score Distribution by Hyperparameter', fontsize=14)\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.94)\n",
    "fig.savefig('figures/sklearn_cv_scores_by_param', dpi=300);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy-encode parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:16.932739Z",
     "start_time": "2020-06-19T23:53:16.916019Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 576 entries, 0 to 575\n",
      "Data columns (total 19 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   test_score                  576 non-null    float64\n",
      " 1   learning_rate_0.01          576 non-null    uint8  \n",
      " 2   learning_rate_0.1           576 non-null    uint8  \n",
      " 3   learning_rate_0.2           576 non-null    uint8  \n",
      " 4   max_depth_3                 576 non-null    uint8  \n",
      " 5   max_depth_6                 576 non-null    uint8  \n",
      " 6   max_depth_9                 576 non-null    uint8  \n",
      " 7   max_depth_12                576 non-null    uint8  \n",
      " 8   max_features_0.8            576 non-null    uint8  \n",
      " 9   max_features_1              576 non-null    uint8  \n",
      " 10  max_features_sqrt           576 non-null    uint8  \n",
      " 11  min_impurity_decrease_0.0   576 non-null    uint8  \n",
      " 12  min_impurity_decrease_0.01  576 non-null    uint8  \n",
      " 13  min_samples_split_10        576 non-null    uint8  \n",
      " 14  min_samples_split_50        576 non-null    uint8  \n",
      " 15  n_estimators_100            576 non-null    uint8  \n",
      " 16  n_estimators_300            576 non-null    uint8  \n",
      " 17  subsample_0.8               576 non-null    uint8  \n",
      " 18  subsample_1.0               576 non-null    uint8  \n",
      "dtypes: float64(1), uint8(18)\n",
      "memory usage: 14.8 KB\n"
     ]
    }
   ],
   "source": [
    "data = get_test_scores(results)\n",
    "params = data.columns[:-1].tolist()\n",
    "data = pd.get_dummies(data,columns=params, drop_first=False)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Regression Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now explore how hyperparameter settings jointly affect the mean cross-validation score. To gain insight into how parameter settings interact, we can train a DecisionTreeRegressor with the mean test score as the outcome and the parameter settings, encoded as categorical variables in one-hot or dummy format. \n",
    "\n",
    "The tree structure highlights that using all features (max_features_1), a low learning_rate, and a max_depth over three led to the best results, as shown in the following diagram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:16.937753Z",
     "start_time": "2020-06-19T23:53:16.933916Z"
    }
   },
   "outputs": [],
   "source": [
    "reg_tree = DecisionTreeRegressor(criterion='mse',\n",
    "                                 splitter='best',\n",
    "                                 max_depth=4,\n",
    "                                 min_samples_split=5,\n",
    "                                 min_samples_leaf=10,\n",
    "                                 min_weight_fraction_leaf=0.0,\n",
    "                                 max_features=None,\n",
    "                                 random_state=42,\n",
    "                                 max_leaf_nodes=None,\n",
    "                                 min_impurity_decrease=0.0,\n",
    "                                 min_impurity_split=None,\n",
    "                                 presort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:16.950472Z",
     "start_time": "2020-06-19T23:53:16.939004Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=4, min_samples_leaf=10, min_samples_split=5,\n",
       "                      presort=False, random_state=42)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm_features = data.drop('test_score', axis=1).columns\n",
    "reg_tree.fit(X=data[gbm_features], y=data.test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:16.957711Z",
     "start_time": "2020-06-19T23:53:16.952020Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32877771, 0.        , 0.00539535, 0.26611295, 0.0109397 ,\n",
       "       0.        , 0.00227883, 0.02879713, 0.23131014, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.12280906,\n",
       "       0.        , 0.        , 0.00357913])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_tree.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:16.997155Z",
     "start_time": "2020-06-19T23:53:16.958926Z"
    }
   },
   "outputs": [
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       "<text text-anchor=\"start\" x=\"707\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">max_features_0.8 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"748.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"738\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32</text>\n",
       "<text text-anchor=\"start\" x=\"738\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.508</text>\n",
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       "<!-- 9&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>9&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M718.9,-192.88C726.67,-183.98 735.17,-174.24 743.26,-164.96\"/>\n",
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       "<!-- 11 -->\n",
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       "<title>11</title>\n",
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       "<text text-anchor=\"start\" x=\"482.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"472\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\n",
       "<text text-anchor=\"start\" x=\"472\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.537</text>\n",
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       "<!-- 10&#45;&gt;11 -->\n",
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       "<title>10&#45;&gt;11</title>\n",
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       "<!-- 12 -->\n",
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       "<title>12</title>\n",
       "<path fill=\"#f4ccaf\" stroke=\"black\" d=\"M666,-53C666,-53 592,-53 592,-53 586,-53 580,-47 580,-41 580,-41 580,-12 580,-12 580,-6 586,0 592,0 592,0 666,0 666,0 672,0 678,-6 678,-12 678,-12 678,-41 678,-41 678,-47 672,-53 666,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"598.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"588\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\n",
       "<text text-anchor=\"start\" x=\"588\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.522</text>\n",
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       "<!-- 10&#45;&gt;12 -->\n",
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       "<title>10&#45;&gt;12</title>\n",
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       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<path fill=\"#fae9dc\" stroke=\"black\" d=\"M782,-53C782,-53 708,-53 708,-53 702,-53 696,-47 696,-41 696,-41 696,-12 696,-12 696,-6 702,0 708,0 708,0 782,0 782,0 788,0 794,-6 794,-12 794,-12 794,-41 794,-41 794,-47 788,-53 782,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"714.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"704\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"704\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.512</text>\n",
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       "<!-- 13&#45;&gt;14 -->\n",
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       "<title>13&#45;&gt;14</title>\n",
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       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<path fill=\"#ffffff\" stroke=\"black\" d=\"M898,-53C898,-53 824,-53 824,-53 818,-53 812,-47 812,-41 812,-41 812,-12 812,-12 812,-6 818,0 824,0 824,0 898,0 898,0 904,0 910,-6 910,-12 910,-12 910,-41 910,-41 910,-47 904,-53 898,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"830.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"820\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"820\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.504</text>\n",
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       "<!-- 13&#45;&gt;15 -->\n",
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       "<title>13&#45;&gt;15</title>\n",
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       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>17</title>\n",
       "<path fill=\"#e99355\" stroke=\"black\" d=\"M1221,-261C1221,-261 1081,-261 1081,-261 1075,-261 1069,-255 1069,-249 1069,-249 1069,-205 1069,-205 1069,-199 1075,-193 1081,-193 1081,-193 1221,-193 1221,-193 1227,-193 1233,-199 1233,-205 1233,-205 1233,-249 1233,-249 1233,-255 1227,-261 1221,-261\"/>\n",
       "<text text-anchor=\"start\" x=\"1077\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">learning_rate_0.01 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1120.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1106\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 144</text>\n",
       "<text text-anchor=\"start\" x=\"1110\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.542</text>\n",
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       "<!-- 16&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>16&#45;&gt;17</title>\n",
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       "<!-- 24 -->\n",
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       "<title>24</title>\n",
       "<path fill=\"#eb9f67\" stroke=\"black\" d=\"M1511,-261C1511,-261 1371,-261 1371,-261 1365,-261 1359,-255 1359,-249 1359,-249 1359,-205 1359,-205 1359,-199 1365,-193 1371,-193 1371,-193 1511,-193 1511,-193 1517,-193 1523,-199 1523,-205 1523,-205 1523,-249 1523,-249 1523,-255 1517,-261 1511,-261\"/>\n",
       "<text text-anchor=\"start\" x=\"1367\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">learning_rate_0.01 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1410.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.538</text>\n",
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       "<!-- 16&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>16&#45;&gt;24</title>\n",
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       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
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       "<text text-anchor=\"start\" x=\"1010.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">max_depth_12 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1042.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1032\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 96</text>\n",
       "<text text-anchor=\"start\" x=\"1035.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.54</text>\n",
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       "<!-- 17&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>17&#45;&gt;18</title>\n",
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       "<!-- 21 -->\n",
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       "<title>21</title>\n",
       "<path fill=\"#e68743\" stroke=\"black\" d=\"M1284,-157C1284,-157 1174,-157 1174,-157 1168,-157 1162,-151 1162,-145 1162,-145 1162,-101 1162,-101 1162,-95 1168,-89 1174,-89 1174,-89 1284,-89 1284,-89 1290,-89 1296,-95 1296,-101 1296,-101 1296,-145 1296,-145 1296,-151 1290,-157 1284,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"1170\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">max_depth_6 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1198.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1188\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\n",
       "<text text-anchor=\"start\" x=\"1188\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.546</text>\n",
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       "<!-- 17&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>17&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1176.32,-192.88C1183,-184.15 1190.29,-174.62 1197.25,-165.51\"/>\n",
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       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"#e9965a\" stroke=\"black\" d=\"M1014,-53C1014,-53 940,-53 940,-53 934,-53 928,-47 928,-41 928,-41 928,-12 928,-12 928,-6 934,0 940,0 940,0 1014,0 1014,0 1020,0 1026,-6 1026,-12 1026,-12 1026,-41 1026,-41 1026,-47 1020,-53 1014,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"946.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"936\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 64</text>\n",
       "<text text-anchor=\"start\" x=\"936\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.541</text>\n",
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       "<!-- 18&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>18&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1039.42,-88.95C1030.04,-79.71 1019.84,-69.67 1010.46,-60.44\"/>\n",
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       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>20</title>\n",
       "<path fill=\"#eb9c64\" stroke=\"black\" d=\"M1130,-53C1130,-53 1056,-53 1056,-53 1050,-53 1044,-47 1044,-41 1044,-41 1044,-12 1044,-12 1044,-6 1050,0 1056,0 1056,0 1130,0 1130,0 1136,0 1142,-6 1142,-12 1142,-12 1142,-41 1142,-41 1142,-47 1136,-53 1130,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1062.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1052\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32</text>\n",
       "<text text-anchor=\"start\" x=\"1052\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.539</text>\n",
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       "<!-- 18&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>18&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1080,-88.95C1081.76,-80.62 1083.66,-71.65 1085.44,-63.2\"/>\n",
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       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>22</title>\n",
       "<path fill=\"#e58139\" stroke=\"black\" d=\"M1246,-53C1246,-53 1172,-53 1172,-53 1166,-53 1160,-47 1160,-41 1160,-41 1160,-12 1160,-12 1160,-6 1166,0 1172,0 1172,0 1246,0 1246,0 1252,0 1258,-6 1258,-12 1258,-12 1258,-41 1258,-41 1258,-47 1252,-53 1246,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1178.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1168\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32</text>\n",
       "<text text-anchor=\"start\" x=\"1168\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.548</text>\n",
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       "<!-- 21&#45;&gt;22 -->\n",
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       "<title>21&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1222,-88.95C1220.24,-80.62 1218.34,-71.65 1216.56,-63.2\"/>\n",
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       "<title>23</title>\n",
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       "<text text-anchor=\"start\" x=\"1294.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1284\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"1284\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.542</text>\n",
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       "<!-- 21&#45;&gt;23 -->\n",
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       "<title>21&#45;&gt;23</title>\n",
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       "<g id=\"node26\" class=\"node\">\n",
       "<title>25</title>\n",
       "<path fill=\"#ea9b62\" stroke=\"black\" d=\"M1500.5,-157C1500.5,-157 1381.5,-157 1381.5,-157 1375.5,-157 1369.5,-151 1369.5,-145 1369.5,-145 1369.5,-101 1369.5,-101 1369.5,-95 1375.5,-89 1381.5,-89 1381.5,-89 1500.5,-89 1500.5,-89 1506.5,-89 1512.5,-95 1512.5,-101 1512.5,-101 1512.5,-145 1512.5,-145 1512.5,-151 1506.5,-157 1500.5,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"1377.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">subsample_1.0 ≤ 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1410.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.539</text>\n",
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       "<title>24&#45;&gt;25</title>\n",
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       "<!-- 28 -->\n",
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       "<title>28</title>\n",
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       "<text text-anchor=\"start\" x=\"1549.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
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       "<title>24&#45;&gt;28</title>\n",
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       "<!-- 26 -->\n",
       "<g id=\"node27\" class=\"node\">\n",
       "<title>26</title>\n",
       "<path fill=\"#eca16c\" stroke=\"black\" d=\"M1478,-53C1478,-53 1404,-53 1404,-53 1398,-53 1392,-47 1392,-41 1392,-41 1392,-12 1392,-12 1392,-6 1398,0 1404,0 1404,0 1478,0 1478,0 1484,0 1490,-6 1490,-12 1490,-12 1490,-41 1490,-41 1490,-47 1484,-53 1478,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1410.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"1400\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.537</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>25&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1441,-88.95C1441,-80.72 1441,-71.85 1441,-63.48\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1444.5,-63.24 1441,-53.24 1437.5,-63.24 1444.5,-63.24\"/>\n",
       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>27</title>\n",
       "<path fill=\"#e99558\" stroke=\"black\" d=\"M1594,-53C1594,-53 1520,-53 1520,-53 1514,-53 1508,-47 1508,-41 1508,-41 1508,-12 1508,-12 1508,-6 1514,0 1520,0 1520,0 1594,0 1594,0 1600,0 1606,-6 1606,-12 1606,-12 1606,-41 1606,-41 1606,-47 1600,-53 1594,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1526.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">mse = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1516\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n",
       "<text text-anchor=\"start\" x=\"1516\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = 0.541</text>\n",
       "</g>\n",
       "<!-- 25&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>25&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1481.57,-88.95C1493.25,-79.43 1505.97,-69.07 1517.58,-59.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1519.87,-62.27 1525.41,-53.24 1515.44,-56.84 1519.87,-62.27\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x7f1eeec1c310>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_file = 'results/gbm_sklearn_tree.dot'\n",
    "dot_data = export_graphviz(reg_tree,\n",
    "                          out_file=out_file,\n",
    "                          feature_names=gbm_features,\n",
    "                          max_depth=4,\n",
    "                          filled=True,\n",
    "                          rounded=True,\n",
    "                          special_characters=True)\n",
    "if out_file is not None:\n",
    "    dot_data = Path(out_file).read_text()\n",
    "\n",
    "graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute Feature Importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-23T13:40:08.199563Z",
     "start_time": "2018-10-23T13:40:08.196698Z"
    }
   },
   "source": [
    "Overfit regression tree to learn detailed rules that classify all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:17.008526Z",
     "start_time": "2020-06-19T23:53:16.998135Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(presort=False, random_state=42)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_tree = DecisionTreeRegressor(criterion='mse',\n",
    "                                 splitter='best',\n",
    "                                 min_samples_split=2,\n",
    "                                 min_samples_leaf=1,\n",
    "                                 min_weight_fraction_leaf=0.0,\n",
    "                                 max_features=None,\n",
    "                                 random_state=42,\n",
    "                                 max_leaf_nodes=None,\n",
    "                                 min_impurity_decrease=0.0,\n",
    "                                 min_impurity_split=None,\n",
    "                                 presort=False)\n",
    "\n",
    "gbm_features = data.drop('test_score', axis=1).columns\n",
    "reg_tree.fit(X=data[gbm_features], y=data.test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart below displays the influence of the hyperparameter settings in producing different outcomes, measured by their feature importance for a decision tree that is grown to its maximum depth. Naturally, the features that appear near the top of the tree also accumulate the highest importance scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:17.228809Z",
     "start_time": "2020-06-19T23:53:17.009559Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbm_fi = (pd.Series(reg_tree.feature_importances_, \n",
    "                    index=gbm_features)\n",
    "          .sort_values(ascending=False))\n",
    "gbm_fi = gbm_fi[gbm_fi > 0]\n",
    "idx = [p.split('_') for p in gbm_fi.index]\n",
    "gbm_fi.index = ['_'.join(p[:-1]) + '=' + p[-1] for p in idx]\n",
    "gbm_fi.sort_values().plot.barh(figsize=(5,5))\n",
    "plt.title('Hyperparameter Importance')\n",
    "sns.despine()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use a linear regression to gain insights into the statistical significance of the linear relationship between hyperparameters and test scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-19T23:53:17.254796Z",
     "start_time": "2020-06-19T23:53:17.229580Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             test_score   R-squared:                       0.403\n",
      "Model:                            OLS   Adj. R-squared:                  0.391\n",
      "Method:                 Least Squares   F-statistic:                     26.95\n",
      "Date:                Fri, 19 Jun 2020   Prob (F-statistic):           3.34e-45\n",
      "Time:                        19:53:17   Log-Likelihood:                 1976.0\n",
      "No. Observations:                 576   AIC:                            -3928.\n",
      "Df Residuals:                     564   BIC:                            -3876.\n",
      "Df Model:                          11                                         \n",
      "Covariance Type:                  HC3                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "const                          0.5213      0.002    322.640      0.000       0.518       0.524\n",
      "learning_rate_0.1              0.0080      0.001      9.293      0.000       0.006       0.010\n",
      "learning_rate_0.2              0.0061      0.001      6.807      0.000       0.004       0.008\n",
      "max_depth_6                    0.0043      0.001      4.333      0.000       0.002       0.006\n",
      "max_depth_9                    0.0071      0.001      6.781      0.000       0.005       0.009\n",
      "max_depth_12                   0.0061      0.001      5.545      0.000       0.004       0.008\n",
      "max_features_1                 0.0081      0.001      9.428      0.000       0.006       0.010\n",
      "max_features_sqrt             -0.0016      0.001     -1.873      0.061      -0.003    7.37e-05\n",
      "min_impurity_decrease_0.01 -6.906e-05      0.001     -0.104      0.917      -0.001       0.001\n",
      "min_samples_split_50           0.0007      0.001      0.997      0.319      -0.001       0.002\n",
      "n_estimators_300               0.0042      0.001      6.278      0.000       0.003       0.005\n",
      "subsample_1.0                  0.0006      0.001      0.845      0.398      -0.001       0.002\n",
      "==============================================================================\n",
      "Omnibus:                       15.164   Durbin-Watson:                   0.971\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               15.665\n",
      "Skew:                          -0.398   Prob(JB):                     0.000397\n",
      "Kurtosis:                       3.139   Cond. No.                         7.90\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC3)\n"
     ]
    }
   ],
   "source": [
    "data = get_test_scores(results)\n",
    "params = data.columns[:-1].tolist()\n",
    "data = pd.get_dummies(data,columns=params, drop_first=True)\n",
    "\n",
    "model = OLS(endog=data.test_score, exog=add_constant(data.drop('test_score', axis=1))).fit(cov_type='HC3')\n",
    "print(model.summary())"
   ]
  }
 ],
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